GAN Inversion.
Given a degraded image \(x\) , GAN inversion-based methods [1]}, [2]}, [3]}, [4]} in general produce a natural image best approximating \(x\)  by optimizing \(z^* = \operatornamewithlimits{argmin}\nolimits _{z \in \mathcal {Z}} \mathcal {L}\left(G(z), x\right)\) , where \(\mathcal {Z}\)  is the latent space and \(\mathcal {L}(\cdot ,\cdot )\)  denotes the task-specific objective function.
For instance, PULSE [3]} iteratively optimizes the latent code of StyleGAN [6]} with a pixel-wise constraint between the input and output.
mGANprior [2]} optimizes multiple latent codes to increase the expressiveness of the model.
DGP [4]} further finetunes the generator together with the latent code to reduce the gap between the distributions of the training and testing images.
A common issue with GAN inversion is that important spatial information may not be faithfully kept due the low-dimensionality of the latent code. Thus, these methods often generate undesirable results that do not resemble the ground-truth.
Different from GAN inversion, GLEAN conditions the pre-trained generator with both the latent codes and multi-resolution convolutional features, providing additional spatial guidance for restoration. In addition, GLEAN does not require iterative optimization during inference.
